MAGE-HEP introduces a GUI-driven workflow environment for reproducible Monte Carlo analyses in high-energy physics organized by project-study-run hierarchy.
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A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
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MAGE-HEP: Monte Carlo Analysis and Graphical Environment for High-Energy Physics
MAGE-HEP introduces a GUI-driven workflow environment for reproducible Monte Carlo analyses in high-energy physics organized by project-study-run hierarchy.
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Inferring identified hadron production in $pp$ collisions with physics-informed machine learning at the LHC
A physics-informed neural network infers pT spectra of pi, K, p, Lambda, and Ks in unmeasured rapidity regions from PYTHIA8 pp collisions at 13.6 TeV, achieving 1.5-5.83% yield uncertainties while reproducing yield ratios and freeze-out parameters.
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Open LHC Monte Carlo Event Generation
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.